DIGITAL COLOR

EURASIP Journal on Image and Video Processing, 2008

The motivation of this paper is to provide an overview of the most recent trends and of the future research directions in color image and video processing. Rather than covering all aspects of the domain this survey covers issues related to the most active research areas in the last two years. It presents the most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging. It also focuses on the most promising research areas in color imaging science. This survey gives an overview about the issues, controversies, and problems of color image science. It focuses on human color vision, perception, and interpretation. It focuses also on acquisition systems, consumer imaging applications, and medical imaging applications. Next it gives a brief overview about the solutions, recommendations, most recent trends, and future trends of color image science. It focuses on color space, appearance models, color difference metrics, and color saliency. It focuses also on color features, color-based object tracking, scene illuminant estimation and color constancy, quality assessment and fidelity assessment, color characterization and calibration of a display device. It focuses on quantization, filtering and enhancement, segmentation, coding and compression, watermarking, and lastly on multispectral color image processing. Lastly, it addresses the research areas which still need addressing and which are the next and future perspectives of color in image and video processing.

IJNTR Journal, 2017

Abstract-The use of color in image processing is motivated by two principal factors. First, color is a powerful descriptor that often simplifies object identification and extraction from a scene. Color image processing is divided into two major areas: full-color and pseudo-color processing. In the first category, the images in question typically are acquired with a full-color sensor, such as a color TV camera or color scanner. In the second category, the problem is on of assigning a color to a particular monochrome intensity or range of intensities. Until recently, most digital color image processing was done at the pseudo color level. However, in the past decade, color sensors and hardware for processing color images have become available at reasonable prices. The result is that full-color image processing techniques are now used in a broad range of applications, including publishing, visualization, and the Internet.

Why do we have colour? What use is it to us? Some of the obvious answers are that we see colour so that we can recognise objects, spot objects more quickly, tell when fruit is ripe or rotten. These reasons make a lot of sense, but are there others? In this paper, we explore the things that colour makes easier for computational vision systems. In particular, we examine the role of colour in understanding specularities, processing interreflections, identifiying metals from plastics and wet surfaces from dry ones, choosing foveation points, disambiguating stereo matches, discriminating textures and identifying objects. Of course, what is easier for a computational vision system is not necessarily the same for the human visual system but it can perhaps help us create some hypotheses about the role of colour in human perception. We also consider the role of colour constancy in terms of whether or not it is required for colour to be useful to a computer vision system.

2008 IEEE International Conference on Automation, Quality and Testing, Robotics, 2008

In the context of he round table the following topics related to image colour processing will be discussed: Historical point of view. Studies of Aguilonius, Gerritsen, Newton and Maxwell. CIE Standard (Commission International de l'Eclaraige). Colour Models. RGB, HIS, etc. Colour segmentation based on HSI model. Industrial applications. Summary and discussion. At the end, video images showing the robustness of colour in front of B/W images will be presented.

The color model is an abstract mathematical model describing the way colors can describe as the tuples of numbers, typically as three or four values or color mechanism (e.g. RGB and CMYK are color models). Nevertheless, a color model with no connected mapping function to a complete color space is a more or less arbitrary color system with no link to any universal unstated system of color clarification. Color spaces supply a normal method to recognize order, influence and successfully display the object colors taken into thoughtfulness. There are various model based on human awareness, on color acknowledgment, on various color device etc. A few papers on various applications such as lane finding, face finding, fruit brilliance appraisal etc based on these color models have been available. A survey on broadly used models RGB, HSI, HSV, RGI etc is represented in this paper.

Journal on Image and Video Processing, 2008

The motivation of this paper is to provide an overview of the most recent trends and of the future research directions in color image and video processing. Rather than covering all aspects of the domain this survey covers issues related to the most active research areas in the last two years. It presents the most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging. It also focuses on the most promising research areas in color imaging science. This survey gives an overview about the issues, controversies, and problems of color image science. It focuses on human color vision, perception, and interpretation. It focuses also on acquisition systems, consumer imaging applications, and medical imaging applications. Next it gives a brief overview about the solutions, recommendations, most recent trends, and future trends of color image science. It focuses on color space, appearance models, color difference metrics, and color saliency. It focuses also on color features, color-based object tracking, scene illuminant estimation and color constancy, quality assessment and fidelity assessment, color characterization and calibration of a display device. It focuses on quantization, filtering and enhancement, segmentation, coding and compression, watermarking, and lastly on multispectral color image processing. Lastly, it addresses the research areas which still need addressing and which are the next and future perspectives of color in image and video processing.

The theoretical outcomes and experimental results of new color model implemented in algorithms and software of image processing are presented in the paper. This model, as it will be shown below, may be used in modern real time video processing applications such as radar tracking and communication systems. The developed model allows carrying out the image process with the least time delays (i.e. it speeding up image processing). The proposed model can be used to solve the problem of true color object identification. Experimental results show that the time spent during RGI color model conversion may approximately four times less than the time spent during other similar models.

In several studies, we have noticed the absence of real study about the influence of the color space (model) in the concept of color object recognition process. Beholding this major topic, and in order to find better representation for the color space recognition, we present this research involving comparison between different color spaces XYZ, LAB, RGB, HSB and HSV used in the colored object recognition process. This study comes in the interest of a suggestion of a new color space adapted to the recognition process (Expanded-LAB). For the extraction process, Zernike Moments was the well-advised choice for extracting big number of signifies parameters, especially useful for databases. We choose the neural networks classifier as a classification method, thanks to its efficiency detection of complex nonlinear relationships between variables, also for its ability to detect possible interactions between predictor variables. 1. Introduction This study falls within the field of image proc...

2002

Most image analysis algorithms are defined for the grey level channel, particularly when geometric information is looked for in the digital image. We propose an experimental procedure in order to decide whether this attitude is sound or not. We test the hypothesis that the essential geometric contents of an image is contained in its level lines. The set of all level lines, or topographic map, is a complete contrast invariant image description: it yields a line structure by far more complete than any edge description, since we can fully reconstruct the image from it, up to a local contrast change. We then design an algorithm constraining the color channels of a given image to have the same geometry (i.e. the same level lines) as the grey level. If the assumption that the essential geometrical information is contained in the grey level is sound, then this algorithm should not alter the colors of the image or its visual aspect. We display several experiments confirming this hypothesis. Conversely, we also show the effect of imposing the color of an image to the topographic map of another one: it results, in a striking way, in the dominance of grey level and the fading of a color deprived of its geometry. We finally give a mathematical proof that the algorithmic procedure is intrinsic, i.e. does not depend asymptotically upon the quantization mesh used for the topographic map. We also prove its contrast invariance.